Athina P. Petropulu is a distinguished Professor at the Electrical and Computer Engineering (ECE) Department at Rutgers, having served as chair of the department during 2010-2016. Prior to joining Rutgers she was a Professor of ECE at Drexel University (1992-2010). She held Visiting Scholar appointments at SUPELEC, Universite’ Paris Sud, Princeton University and University of Southern California. Dr. Petropulu's research interests span the area of statistical signal processing, wireless communications, signal processing in networking, physical layer security, and radar signal processing. Her research has been funded by various government industry sponsors including the National Science Foundation (NSF), the Office of Naval research, the US Army, the National Institute of Health, the Whitaker Foundation, Lockheed Martin and Raytheon.
Dr. Petropulu is Fellow of IEEE and AAAS and recipient of the 1995 Presidential Faculty Fellow Award given by NSF and the White House. She is President-Elect for the IEEE Signal Processing Society for 2020-2021. She has served as Editor-in-Chief of the IEEE Transactions on Signal Processing (2009-2011), IEEE Signal Processing Society Vice President-Conferences (2006-2008), and is currently member-at-large of the IEEE Signal Processing Board of Governors. She was the General Chair of the 2005 International Conference on Acoustics Speech and Signal Processing (ICASSP-05), Philadelphia PA, and is General Co-Chair of the 2018 IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC). She is recipient of the 2005 IEEE Signal Processing Magazine Best Paper Award, and the 2012 IEEE Signal Processing Society Meritorious Service Award. She was Distinguished Lecturer for the Signal Processing Society for 2017-2018, and is currently Distinguished Lecturer for the IEEE Aerospace & Electronics Systems Society.
DUAL FUNCTION RADAR-COMMUNICATION SYSTEMS
Automotive radars for advanced driver assistance systems and autonomous driving are required to have high angle discrimination capability and small package size so that they can be easily integrated into vehicles. Unlike conventional phase arrays whose resolution is proportional to their size, multi-input multi-output (MIMO) radar can meet both high resolution and small size requirements. This is because MIMO radar can synthesize virtual arrays with large apertures using only small number of transmit and receive antennas. Even with the help of MIMO radartechnology, however, the cost of synthesizing a large virtual uniform linear array (ULA) with half wavelength element spacing can be very high. One way to further reduce the cost without sacrificing angle resolution is to use virtual sparse linear arrays (SLAs), e.g., use a thinned receive ULA. SLA operating as a MIMO radar can properly deploy the reduced number of transmit and receive antennas, such that the element spacing of the corresponding virtual array is larger than half wavelength, while its aperture is the same as that of a ULA with half wavelength element spacing.Prior approaches have focused on optimal sparse array design, or use of interpolation techniques for filling theholes in the synthesized SLA before applying digital beamforming for angle finding. In this talk, we present a new approach, where we use matrix completion to complete the corresponding virtual ULA before estimating the target angle. In particular, we show that for a small number of targets within the same range-Doppler cell, the Hankel matrix constructed by subarrays of the virtual ULA is low-rank, and thus under certain conditions, can be completed based on the SLA measurements. We derive the coherence properties of the Hankel matrix so that it can be competed via nuclear norm minimization methods. We also demonstrate via examples the effect of various SLA topologies on the identifiability of the Hankel matrix.